<p align="center">
<h1 align="center">Fast-SSIM</h1>
<p align="center">
Speed up your SSIM and PSNR calculations!
<br />
</p>
</p>
<br>
## ℹ About
This fork wraps the Fast-SSIM project in an easy to use package that can be readily installed via PyPI.
The Fast-SSIM package can accelerate your SSIM and PSNR calculations by up to 30x and 10x respectively.
## Requirements
- Python 3.9 or higher
## How to Install
```
pip install Fast-SSIM
```
## Usage
The functionalities are explained in the following code snippet that is also provided in this repo:
```python
import fast_ssim
from skimage.io import imread
img1_path = r"test_images\0.jpg"
img2_path = r"test_images\1.jpg"
# Load the images into NumPy arrays
img1 = imread(img1_path)
img2 = imread(img2_path)
ssim_score = fast_ssim.ssim(
img1, img2,
data_range=255
)
psnr_score = fast_ssim.psnr(
img1, img2,
data_range=255
)
print(f"SSIM Score: {ssim_score}")
print(f"PSNR Score: {psnr_score}")
```
Output:
```
SSIM Score: 0.9886590838432312
PSNR Score: 31.11587142944336
```
## Notes
- The SSIM calculation uses sample covariance (and sample variance) for its statistics. This aligns with the default behavior of `scikit-image`'s `structural_similarity` function, where `use_sample_covariance` is True by default.
- This implementation does not offer an option for applying a Gaussian filter to the images or local windows prior to the SSIM/PSNR calculation.
## Original author
Chen Yu / [@Chen Yu](https://github.com/chinue)
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"description": "<p align=\"center\">\r\n <h1 align=\"center\">Fast-SSIM</h1>\r\n <p align=\"center\">\r\n Speed up your SSIM and PSNR calculations!\r\n <br />\r\n </p>\r\n</p>\r\n\r\n<br>\r\n\r\n## \u2139 About\r\n\r\nThis fork wraps the Fast-SSIM project in an easy to use package that can be readily installed via PyPI.\r\nThe Fast-SSIM package can accelerate your SSIM and PSNR calculations by up to 30x and 10x respectively.\r\n\r\n## Requirements\r\n\r\n- Python 3.9 or higher\r\n\r\n## How to Install\r\n\r\n```\r\npip install Fast-SSIM\r\n```\r\n\r\n## Usage\r\n\r\nThe functionalities are explained in the following code snippet that is also provided in this repo:\r\n\r\n```python\r\nimport fast_ssim\r\nfrom skimage.io import imread\r\n\r\nimg1_path = r\"test_images\\0.jpg\"\r\nimg2_path = r\"test_images\\1.jpg\"\r\n\r\n# Load the images into NumPy arrays\r\nimg1 = imread(img1_path)\r\nimg2 = imread(img2_path)\r\n\r\nssim_score = fast_ssim.ssim(\r\n img1, img2,\r\n data_range=255\r\n)\r\npsnr_score = fast_ssim.psnr(\r\n img1, img2,\r\n data_range=255\r\n)\r\n\r\nprint(f\"SSIM Score: {ssim_score}\")\r\nprint(f\"PSNR Score: {psnr_score}\")\r\n```\r\n\r\nOutput:\r\n```\r\nSSIM Score: 0.9886590838432312\r\nPSNR Score: 31.11587142944336\r\n```\r\n\r\n## Notes\r\n\r\n- The SSIM calculation uses sample covariance (and sample variance) for its statistics. This aligns with the default behavior of `scikit-image`'s `structural_similarity` function, where `use_sample_covariance` is True by default.\r\n\r\n- This implementation does not offer an option for applying a Gaussian filter to the images or local windows prior to the SSIM/PSNR calculation.\r\n\r\n## Original author\r\n\r\nChen Yu / [@Chen Yu](https://github.com/chinue)\r\n",
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